Decoding the semantic content of natural movies from human brain activity (Huth et al., Frontiers in Systems Neuroscience, 2016)

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Several recent neuroimaging studies have decoded the structure or semantic content of static visual images from human brain activity. This paper presents a decoding algorithm, hierarchical logistic regression (HLR), that makes it possible to decode detailed information about the object and action categories present in natural movies from human brain activity signals measured by functional MRI. The model decodes the present of many object and action categories from fMRI responses with a high degree of accuracy. This framework can also be used to test whether semantic relationships defined in the WordNet taxonomy are represented the same way in the human brain. Hierarchical relationships between general categories and atypical examples, such as organism and plant, did not seem to be reflected in brain representations measured by fMRI. Get the paper here.